• Management & Information Systems Review
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• v.35 no.1
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• pp.51-68
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• 2016

In this paper, we aim to analyze the effect of accounting policy change subsequent to the adoption of K-IFRS in Korea, whereby the firms are required to recognize impairment losses on goodwill on a periodic basis rather than to amortize over a specific period. As a principle-based accounting standard, the K-IFRS expands the scope of fair value measurement with a view to enhance the relevance and timeliness of accounting information. In the same vein, intangibles with indefinite useful life, of which goodwill is an example, are subject to regulatory impairment tests at least once a year. Related literature on the impact of mandatory change in goodwill policy document that impairment recognition is more likely to be practiced opportunistically, mainly because managers have a greater discretion to conduct the tests under K-IFRS. However, existing literature examined the frequency and/or magnitude of the goodwill impairment before versus after the K-IFRS adoption, failing to notice the impairment symptoms at individual firm level. Borrowing the definition of impairment symptoms suggested by Ramanna and Watts(2012), this study performs a series of tests to determine whether the goodwill impairment recognition achieves the goal of communicating timelier information under the K-IFRS regime. Using 947 firm-year observations from domestic companies listed in KRX and KOSDAQ markets from 2008 to 2011, we document overall delays in recognizing impairment losses on goodwill after the adoption of K-IFRS relative to prior period, based on logistic and OLS regression analyses. The results are qualitatively similar in robustness tests, which use alternative proxy for goodwill impairment symptom. Afore-mentioned results indicate that managers are likely to take advantage of the increased discretion to recognize the impairment losses on goodwill rather than to provide timelier information on impairment, inconsistent with the goal of regulatory authority, which is in line with the improvement of timeliness and relevance of accounting information in conjunction with the full implementation of K-IFRS. This study contributes to the extant literature on goodwill impairment from a methodological viewpoint. We believe that the method employed in this paper potentially diminishes the bias inherent in researches relying on ex post impairment recognition, by conducting tests based on ex ante impairment symptoms, which allows direct examination of the timeliness changes between before and after K-IFRS adoption.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.